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‎README.md‎

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# (Replace_with_your_title) Cookbook
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# Bias Correction of Crowdsourced Bicycle Count Data Cookbook
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<img src="thumbnails/thumbnail.png" alt="thumbnail" width="300"/>
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<img src="thumbnails/biascorr_thumbnail.png" alt="thumbnail" width="300"/>
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[![nightly-build](https://github.com/ProjectPythia/cookbook-template/actions/workflows/nightly-build.yaml/badge.svg)](https://github.com/ProjectPythia/cookbook-template/actions/workflows/nightly-build.yaml)
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[![Binder](https://binder.projectpythia.org/badge_logo.svg)](https://binder.projectpythia.org/v2/gh/ProjectPythia/cookbook-template/main?labpath=notebooks)
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[![Binder](https://binder.projectpythia.org/badge_logo.svg)](https://binder.projectpythia.org/v2/gh/ProjectPythia/BiasCorrectionCrowdsourcedData-cookbook/main?labpath=notebooks)
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[![DOI](https://zenodo.org/badge/475509405.svg)](https://zenodo.org/badge/latestdoi/475509405)
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_See the [Cookbook Contributor's Guide](https://projectpythia.org/cookbook-guide) for step-by-step instructions on how to create your new Cookbook and get it hosted on the [Pythia Cookbook Gallery](https://cookbooks.projectpythia.org)!_
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This Project Pythia Cookbook covers ... (replace `...` with the main subject of your cookbook ... e.g., _working with radar data in Python_)
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This Project Pythia Cookbook covers **bias correction of crowdsourced bicycle ridership data (Strava) using traditional count sources (EcoCounter)** in Northwest Arkansas, incorporating spatial clustering, multivariate regression, and residual analysis.
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## Motivation
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(Add a few sentences stating why this cookbook will be useful. What skills will you, "the chef", gain once you have reached the end of the cookbook?)
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This cookbook provides a reproducible workflow to identify, quantify, and correct bias in Strava bicycle count data using EcoCounter ground-truth data. You'll learn how to clean and merge weekly count data, engineer features, model spatial-temporal patterns, and evaluate model performance using regression and clustering methods.
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## Authors
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[First Author](https://github.com/first-author1), [Second Author](https://github.com/second-author2), etc. _Acknowledge primary content authors here_
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[Nelofar Qulizada](https://github.com/nqulizada835)
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### Contributors
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## Structure
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(State one or more sections that will comprise the notebook. E.g., _This cookbook is broken up into two main sections - "Foundations" and "Example Workflows."_ Then, describe each section below.)
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This cookbook is broken up into two main sections: **"Data Preprocessing and Exploratory Analysis"** and **"Modeling and Bias Correction"**.
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### Section 1 ( Replace with the title of this section, e.g. "Foundations" )
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### Section 1: Data Preprocessing and Exploratory Analysis
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(Add content for this section, e.g., "The foundational content includes ... ")
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Includes:
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- Loading and merging weekly EcoCounter and Strava data
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- Creating spatial hexbin aggregations
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- Visualizing counts over time and space
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- Exploring potential covariates
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### Section 2 ( Replace with the title of this section, e.g. "Example workflows" )
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### Section 2: Modeling and Bias Correction
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(Add content for this section, e.g., "Example workflows include ... ")
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Includes:
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- Building linear regression models to predict EcoCounter counts
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- Performing seasonal regression breakdowns
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- Visualizing spatial clustering of residuals
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- Generating interactive maps and outputs
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## Running the Notebooks
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important for now. All you need to know is how to launch a Pythia
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Cookbooks chapter via Binder. Simply navigate your mouse to
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the top right corner of the book chapter you are viewing and click
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on the rocket ship icon, (see figure below), and be sure to select
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on the rocket ship icon, and be sure to select
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“launch Binder”. After a moment you should be presented with a
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notebook that you can interact with. I.e. you’ll be able to execute
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and even change the example programs. You’ll see that the code cells
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have no output at first, until you execute them by pressing
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{kbd}`Shift`\+{kbd}`Enter`. Complete details on how to interact with
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a live Jupyter notebook are described in [Getting Started with
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Jupyter](https://foundations.projectpythia.org/foundations/getting-started-jupyter).
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notebook that you can interact with.
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Note, not all Cookbook chapters are executable. If you do not see
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the rocket ship icon, such as on this page, you are not viewing an
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executable book chapter.
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### Running on Your Own Machine
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If you are interested in running this material locally on your computer, you will need to follow this workflow:
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(Replace "cookbook-example" with the title of your cookbooks)
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If you are interested in running this material locally on your computer, follow this workflow:
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1. Clone the `https://github.com/ProjectPythia/cookbook-example` repository:
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```bash
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git clone https://github.com/ProjectPythia/cookbook-example.git
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```
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1. Move into the `cookbook-example` directory
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```bash
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cd cookbook-example
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```
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1. Create and activate your conda environment from the `environment.yml` file
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```bash
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conda env create -f environment.yml
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conda activate cookbook-example
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```
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1. Move into the `notebooks` directory and start up Jupyterlab
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1. Clone the repository:
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```bash
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cd notebooks/
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jupyter lab
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```
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git clone https://github.com/ProjectPythia/BiasCorrectionCrowdsourcedData-cookbook.git

‎_gallery_info.yml‎

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thumbnail: thumbnail.png
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thumbnail: BiasCorrectionCrowdsourcedData-cookbook/thumbnails/biascorr_thumbnail.png
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tags:
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domains:
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- sampledomain
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- transportation
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- urban-analytics
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- geospatial-analysis
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packages:
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- samplepackage
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- pandas
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- numpy
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- matplotlib
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- seaborn
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- scikit-learn
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- folium
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‎environment.yml‎

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name: biascorr
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name: biascorr-cookbook-dev
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channels:
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- conda-forge
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dependencies:
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- python=3.11
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- pandas
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- geopandas
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- numpy
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- h3
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- matplotlib
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- statsmodels
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- scikit-learn
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- jupyter
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- jupyter
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- mystmd
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- jupyterlab-myst

‎figures/obs_vs_pred_test.png‎

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